integrating capacity and logistics of large additive manufacturing networks

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Negotiating and Sharing Capacities of Large Additive Manufacturing Networks

This paper focuses on dynamics of productive and demanding nodes for Scattered Manufacturing Networks within 3D Printings contexts. The various nodes issue orders or sell production slots in order to achieve their own aims. An orchestrator coordinates the dynamics along the network according to principles of sustainability, equated shared resources and transparency by managing communication activities among nodes. In particular, suitable tradeoffs occur by a unique framework that, with the aim of optimizing the overall costs, suggests either logistics paths along the network or negotiation policies among nodes in order to reallocate resources. Numerical examples present the proposed approach. Keywords: Industry 4.0, Additive Manufacturing, Sharing Capacities, Operation Models, Optimization of networks JEL Codes: C02; O21 and P4

An Analytical Model for Optimizing the Combination of Energy Sources in a Single Power Transmission Network

The increasing amount of renewable energy currently being added to distribution networks presents new challenges and opportunities to system operators. This situation further complicates the operators' tasks in dealing with changing net loads and balancing. The current work provides an analytical model to assist systems operators in stabilizing power generation and lowering total costs, through optimization of choices in the combination of programmable fossil sources and nonprogrammable renewable sources. The study first examines the various programmable and renewable energy sources that appear broadly suitable and economically appealing for combination. Next we identify the most important factors determining the potential integration of the sources in the system. Based on this introductory information we then develop the model for the selection of the appropriate mix of sources to achieve stable production. In developing the model we define indicators to evaluate and select the best configurations of the sources included in a particular combination. Next we apply the model to a specific case study and finally reexamine the interdependencies among all the variables of the model, to provide a better understanding of its dynamics and results. Variability in Energy Sources and Loads Electricity cannot be stored on a massive scale in an economical way; thus system operators must constantly balance power supply and demand to maintain overall stability and power quality. Serious mismatches could cause local power interruptions, blackouts, or breakdowns in the entire system. Conventional hydroelectricity and coal, oil, or gas thermal generation provide steady and predictable feeds to the energy grids, with precise scheduling of output. On the other hand, renewable energy sources such as wind and solar power are typically variable, meaning that they provide intermittent output that cannot be completely and accurately predicted. The increasing application of renewable energy technologies to feed into power grids is a challenge to the system operators, who must then deal with more unpredictable net loads and more complex balancing Possible solutions include (1) energy storage (e.g., pumped hydroelectric and compressed air energy storage, chemical batteries, and active load management) [3]; (2) geographic diversification of installation sites; (3) combination of energy sources. The option of energy storage as the solution to programming still presents important technical and financial limits. Diversification of renewable sources and installations can indeed reduce overall variability; however it does not eliminate the intrinsic variability of the sources. Thus one of the current challenges for network managers is the question of how to combine various energy sources in a manner that best controls variability over the entire distribution system. Possibilities and Advantages of Combining Energy Sources To combine energy sources means to integrate two or more power plants drawing on different sources. The aim of ou

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Statistical physics of vaccination

Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination–one of the most important preventive measures of modern times–is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research